Timeline for Standard error of the quotient of two estimates (Wald estimators) using the delta method
Current License: CC BY-SA 3.0
17 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 9, 2013 at 21:00 | comment | added | dimitriy | I don't believe the covariance term should be zero. | |
| Jun 9, 2013 at 19:53 | comment | added | dimitriy | There are two posts on Statalist that deal with this exact problem: stata.com/statalist/archive/2009-10/index.html#00467 and stata.com/statalist/archive/2009-10/index.html#00670. The second one deals with the standard errors. | |
| Jun 9, 2013 at 13:38 | comment | added | user1690130 | I should have mentioned this at the onset, but in particular, I'm interested in constructing a Wald estimator. In that case, is the covariance zero or non-zero? | |
| Jun 6, 2013 at 16:28 | comment | added | dimitriy | You can also just read these off from the regression output. The covariances are not automatically shown, nor is there a particularly quick way of getting them. But you can get the variance-covariance matrix by typing "estat vce" and just read them off. Note that this already does the squaring for you for the variances. Compare what you see after each regression to what you get after sureg. You'll see that you get the covariance only with sureg. | |
| Jun 6, 2013 at 16:26 | comment | added | dimitriy | That's the $Cov[X,Y]$ term in Greg_b's formula. The $E[]$s are the coefficients, which Stata calls _b[varname]. The $Var[]$s are squares of the standard errors of the coefficients, which Stata calls _se[varname]. | |
| Jun 6, 2013 at 15:05 | comment | added | user1690130 | You suggested sureg. So you are saying to get a sureg term to get a covariance between the two estimators? I did not realize I needed a covariance term. | |
| Jun 6, 2013 at 14:54 | comment | added | dimitriy | I am not sure what conducive to SUR means. Without some sort of systems approach, there's no covariance term. Where would it come from? | |
| Jun 6, 2013 at 14:50 | comment | added | user1690130 | By SUR, I meant seemingly unrelated regressions as you said. I guess I just meant is whether there is a formula with delta method. I am hesistant about SUR because if I am not normally doing my regressions with SUR why would I want to now? | |
| Jun 6, 2013 at 13:55 | comment | added | user1690130 | What if the two regressions are not conducive to SUR? Is there a formula that I could use? Or, must the two equations be conducive to SUR? | |
| Jun 5, 2013 at 17:37 | history | edited | dimitriy | CC BY-SA 3.0 |
added 61 characters in body
|
| Jun 5, 2013 at 17:25 | history | edited | dimitriy | CC BY-SA 3.0 |
added 120 characters in body
|
| Jun 5, 2013 at 16:34 | comment | added | dimitriy | See my response above. | |
| Jun 5, 2013 at 16:33 | history | edited | dimitriy | CC BY-SA 3.0 |
added 2565 characters in body
|
| Jun 5, 2013 at 1:54 | comment | added | user1690130 | Thank you!! Is there a way to do something similar if the estimates are based on coefficients from two separate regressions? | |
| Jun 5, 2013 at 0:03 | history | edited | dimitriy | CC BY-SA 3.0 |
added 8 characters in body
|
| Jun 4, 2013 at 23:41 | comment | added | dimitriy |
If you want to skip to the hypothesis test, you can just testnl _b[x1]/_b[x2]=.5 after the regression.
|
|
| Jun 4, 2013 at 23:33 | history | answered | dimitriy | CC BY-SA 3.0 |